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<h1>𝔐 mpc-openai ✧</h1>
<p><em>MCP Client with OpenAI compatible API</em></p>
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> Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications.
>
> — [https://modelcontextprotocol.io](https://modelcontextprotocol.io)
______________________________________________________________________
> [!WARNING]
> This is a simple toy project. Support is not planned. Use as a reference for minimal MCP client development.
This is a MCP **client** (not a server). It is meant to be used as a library for building LLMs UI that support MCP through an OpenAI compatible API. This opens the door to locally runnable inference engines ([vLLM](https://github.com/vllm-project/vllm), [Ollama](https://github.com/ollama/ollama), [TGI](https://github.com/huggingface/text-generation-inference), [llama.cpp](https://github.com/ggerganov/llama.cpp), [LMStudio](https://github.com/lmstudio-ai), ...) that support providing support for the OpenAI API ([text generation](https://platform.openai.com/docs/guides/text-generation), [function calling](https://platform.openai.com/docs/guides/function-calling), etc.).
## Usage
It is highly recommended to use [uv](https://docs.astral.sh/uv/) in your project based on mpc-openai:
- It manages python installation and virtual environment.
- It is an executable that can run self-contained python scripts (in our case MCP server)
- It is used for CI workflows.
Add mcp-openai to your project dependencies with:
```
uv add mcp-openai
```
or use classic pip install.
### Create a MCP client
Now you can create a MCP client by specifying your custom configuration.
```python
from mcp_openai import MCPClient
from mcp_openai import config
mcp_client_config = config.MCPClientConfig(
mcpServers={
"the-name-of-the-server": config.MCPServerConfig(
command="uv",
args=["run", "path/to/server/scripts.py/or/github/raw"],
)
# add here other servers ...
}
)
llm_client_config = config.LLMClientConfig(
api_key="api-key-for-auth",
base_url="https://api.openai.com/v1",
)
llm_request_config = config.LLMRequestConfig(model=os.environ["MODEL_NAME"])
client = MCPClient(
mcp_client_config,
llm_client_config,
llm_request_config,
)
```
### Connect and process messages with MCP client
```python
async def main():
# Establish connection between the client and the server.
await client.connect_to_server(server_name)
# messages_in are coming from user interacting with the LLM
# e.g. UI making use of this MCP client.
messages_in = ...
messages_out = await client.process_messages(messages_in)
# messages_out contains the LLM response. If required, the LLM make use of
# the available tools offered by the connected servers.
```
